OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating the Efficacy of Cut-and-Paste Data Augmentation in Semantic Segmentation for Satellite Imagery
Venue: arXiv cs.CV 2025
Year: 2025

Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification to categorize each pixel. Despite the advancements brought about by Deep Neural Networks (DNNs), their performance in segmentation tasks is hindered by challenges such as limited availability of labeled data, class imbalance and the inherent variability and complexity of satellite images. In order to mitigate those issues, our study explores the effectiveness of a Cut-and-Paste augmentation technique for semantic segmentation in satellite images. We adapt this augmentation, which usually requires labeled instances, to the case of semantic segmentation. By leveraging the connected components in the semantic segmentation labels, we extract instances that are then randomly pasted during training. Using the DynamicEarthNet dataset and a U-Net model for evaluation, we found that this augmentation significantly enhances the mIoU score on the test set from 37.9 to 44.1. This finding highlights the potential of the Cut-and-Paste augmentation to improve the generalization capabilities of semantic segmentation models in satellite imagery.
Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Planning a network of public transit routes is a challenging optimization problem. Metaheuristic algorithms search through the space of possible transit networks by applying heuristics that randomly alter routes in a network. Existing algorithms almost exclusively use heuristics that modify the network in purely random ways. In this work, we explore whether we can obtain better transit networks using more intelligent heuristics, that modify networks according to a learned preference function instead of at random. We use reinforcement learning to train graph neural nets to act as heuristics. These neural heuristics yield improved results on benchmark synthetic cities with 70 nodes or more, and achieve new state-of-the-art results on the challenging Mumford benchmark. They also improve upon a simulation of the real transit network in the city of Laval, Canada, achieving cost savings of up to 19% over the city's existing transit network.
AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

Graph-based approximate nearest neighbor search (ANNS) algorithms work effectively against large-scale vector retrieval. Among such methods, DiskANN achieves good recall-speed tradeoffs using both DRAM and storage. DiskANN adopts product quantization (PQ) to reduce memory usage, which is still proportional to the scale of datasets. In this paper, we propose All-in-Storage ANNS with Product Quantization (AiSAQ), which offloads compressed vectors to the SSD index. Our method achieves $\sim$10 MB memory usage in query search with billion-scale datasets without critical latency degradation. AiSAQ also reduces the index load time for query search preparation, which enables fast switch between muitiple billion-scale indices.This method can be applied to retrievers of retrieval-augmented generation (RAG) and be scaled out with multiple-server systems for emerging datasets. Our DiskANN-based implementation is available on GitHub.
A Dataset and Framework for Learning State-invariant Object Representations
Venue: arXiv cs.CV 2025
Year: 2025

We add one more invariance - the state invariance - to the more commonly used other invariances for learning object representations for recognition and retrieval. By state invariance, we mean robust with respect to changes in the structural form of the objects, such as when an umbrella is folded, or when an item of clothing is tossed on the floor. In this work, we present a novel dataset, ObjectsWithStateChange, which captures state and pose variations in the object images recorded from arbitrary viewpoints. We believe that this dataset will facilitate research in fine-grained object recognition and retrieval of 3D objects that are capable of state changes. The goal of such research would be to train models capable of learning discriminative object embeddings that remain invariant to state changes while also staying invariant to transformations induced by changes in viewpoint, pose, illumination, etc. A major challenge in this regard is that instances of different objects (both within and across different categories) under various state changes may share similar visual characteristics and therefore may be close to one another in the learned embedding space, which would make it more difficult to discriminate between them. To address this, we propose a curriculum learning strategy that progressively selects object pairs with smaller inter-object distances in the learned embedding space during the training phase. This approach gradually samples harder-to-distinguish examples of visually similar objects, both within and across different categories. Our ablation related to the role played by curriculum learning indicates an improvement in object recognition accuracy of 7.9% and retrieval mAP of 9.2% over the state-of-the-art on our new dataset, as well as three other challenging multi-view datasets such as ModelNet40, ObjectPI, and FG3D.
Computing the $D$-base and $D$-relation in finite closure systems
Venue: arXiv cs.DS 2025
Year: 2025

Implicational bases (IBs) are a common representation of finite closure systems and lattices, along with meet-irreducible elements. They appear in a wide variety of fields ranging from logic and databases to Knowledge Space Theory. Different IBs can represent the same closure system. Therefore, several IBs have been studied, such as the canonical and canonical direct bases. In this paper, we investigate the $D$-base, a refinement of the canonical direct base. It is connected with the $D$-relation, an essential tool in the study of free lattices. The $D$-base demonstrates desirable algorithmic properties, and together with the $D$-relation, it conveys essential properties of the underlying closure system. Hence, computing the $D$-base and the $D$-relation of a closure system from another representation is crucial to enjoy its benefits. However, complexity results for this task are lacking. In this paper, we give algorithms and hardness results for the computation of the $D$-base and $D$-relation. Specifically, we establish the $NP$-completeness of finding the $D$-relation from an arbitrary IB; we give an output-quasi-polynomial time algorithm to compute the $D$-base from meet-irreducible elements; and we obtain a polynomial-delay algorithm computing the $D$-base from an arbitrary IB. These results complete the picture regarding the complexity of identifying the $D$-base and $D$-relation of a closure system.
Meta4XNLI: A Crosslingual Parallel Corpus for Metaphor Detection and Interpretation
Venue: arXiv cs.CL 2025
Year: 2025

Metaphors are a ubiquitous but often overlooked part of everyday language. As a complex cognitive-linguistic phenomenon, they provide a valuable means to evaluate whether language models can capture deeper aspects of meaning, including semantic, pragmatic, and cultural context. In this work, we present Meta4XNLI, the first parallel dataset for Natural Language Inference (NLI) newly annotated for metaphor detection and interpretation in both English and Spanish. Meta4XNLI facilitates the comparison of encoder- and decoder-based models in detecting and understanding metaphorical language in multilingual and cross-lingual settings. Our results show that fine-tuned encoders outperform decoders-only LLMs in metaphor detection. Metaphor interpretation is evaluated via the NLI framework with comparable performance of masked and autoregressive models, which notably decreases when the inference is affected by metaphorical language. Our study also finds that translation plays an important role in the preservation or loss of metaphors across languages, introducing shifts that might impact metaphor occurrence and model performance. These findings underscore the importance of resources like Meta4XNLI for advancing the analysis of the capabilities of language models and improving our understanding of metaphor processing across languages. Furthermore, the dataset offers previously unavailable opportunities to investigate metaphor interpretation, cross-lingual metaphor transferability, and the impact of translation on the development of multilingual annotated resources.
VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

Recognising emotions in context involves identifying an individual's apparent emotions while considering contextual cues from the surrounding scene. Previous approaches to this task have typically designed explicit scene-encoding architectures or incorporated external scene-related information, such as captions. However, these methods often utilise limited contextual information or rely on intricate training pipelines to decouple noise from relevant information. In this work, we leverage the capabilities of Vision-and-Large-Language Models (VLLMs) to enhance in-context emotion classification in a more straightforward manner. Our proposed method follows a simple yet effective two-stage approach. First, we prompt VLLMs to generate natural language descriptions of the subject's apparent emotion in relation to the visual context. Second, the descriptions, along with the visual input, are used to train a transformer-based architecture that fuses text and visual features before the final classification task. This method not only simplifies the training process but also significantly improves performance. Experimental results demonstrate that the textual descriptions effectively guide the model to constrain the noisy visual input, allowing our fused architecture to outperform individual modalities. Our approach achieves state-of-the-art performance across three datasets, BoLD, EMOTIC, and CAER-S, without bells and whistles. The code will be made publicly available on github: https://github.com/NickyFot/EmoCommonSense.git
Near Optimal Alphabet-Soundness Tradeoff PCPs
Venue: arXiv cs.CC 2025
Year: 2025

We show that for all $\varepsilon>0$, for sufficiently large $q\in\mathbb{N}$ power of $2$, for all $δ>0$, it is NP-hard to distinguish whether a given $2$-Prover-$1$-Round projection game with alphabet size $q$ has value at least $1-δ$, or value at most $1/q^{1-\varepsilon}$. This establishes a nearly optimal alphabet-to-soundness tradeoff for $2$-query PCPs with alphabet size $q$, improving upon a result of [Chan, Journal of the ACM 2016]. Our result has the following implications: 1) Near optimal hardness for Quadratic Programming: it is NP-hard to approximate the value of a given Boolean Quadratic Program within factor $(\log n)^{1 - o(1)}$ under quasi-polynomial time reductions. This improves upon a result of [Khot, Safra, ToC 2013] and nearly matches the performance of the best known algorithms due to [Megretski, IWOTA 2000], [Nemirovski, Roos, Terlaky, Mathematical programming 1999] and [Charikar, Wirth, FOCS 2004] that achieve $O(\log n)$ approximation ratio. 2) Bounded degree $2$-CSPs: under randomized reductions, for sufficiently large $d>0$, it is NP-hard to approximate the value of $2$-CSPs in which each variable appears in at most $d$ constraints within factor $(1-o(1))\frac{d}{2}$, improving upon a result of [Lee, Manurangsi, ITCS 2024]. 3) Improved hardness results for connectivity problems: using results of [Laekhanukit, SODA 2014] and [Manurangsi, Inf. Process. Lett., 2019], we deduce improved hardness results for the Rooted $k$-Connectivity Problem, the Vertex-Connectivity Survivable Network Design Problem and the Vertex-Connectivity $k$-Route Cut Problem.
Exploring the Decentraland Economy: Multifaceted Parcel Attributes, Key Insights, and Benchmarking
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents a comprehensive Decentraland parcels dataset, called IITP-VDLand, sourced from diverse platforms such as Decentraland, OpenSea, Etherscan, Google BigQuery, and various Social Media Platforms. Unlike existing datasets which have limited attributes and records, IITP-VDLand offers a rich array of attributes, encompassing parcel characteristics, trading history, past activities, transactions, and social media interactions. Alongside, we introduce a key attribute in the dataset, namely Rarity score, which measures the uniqueness of each parcel within the virtual world. Addressing the significant challenge posed by the dispersed nature of this data across various sources, we employ a systematic approach, utilizing both available APIs and custom scripts, to gather it. Subsequently, we meticulously curate and organize the information into four distinct fragments: (1) Characteristics, (2) OpenSea Trading History, (3) Ethereum Activity Transactions, and (4) Social Media. We envisage that this dataset would serve as a robust resource for training machine- and deep-learning models specifically designed to address real-world challenges within the domain of Decentraland parcels. The performance benchmarking of more than 20 state-of-the-art price prediction models on our dataset yields promising results, achieving a maximum R2 score of 0.8251 and an accuracy of 74.23% in case of Extra Trees Regressor and Classifier. The key findings reveal that the ensemble models perform better than both deep learning and linear models for our dataset. We observe a significant impact of coordinates, geographical proximity, rarity score, and few other economic indicators on the prediction of parcel prices.
Flatness Improves Backbone Generalisation in Few-shot Classification
Venue: arXiv cs.LG 2025
Year: 2025

Deployment of deep neural networks in real-world settings typically requires adaptation to new tasks with few examples. Few-shot classification (FSC) provides a solution to this problem by leveraging pre-trained backbones for fast adaptation to new classes. However, approaches for multi-domain FSC typically result in complex pipelines aimed at information fusion and task-specific adaptation without consideration of the importance of backbone training. In this work, we introduce an effective strategy for backbone training and selection in multi-domain FSC by utilizing flatness-aware training and fine-tuning. Our work is theoretically grounded and empirically performs on par or better than state-of-the-art methods despite being simpler. Further, our results indicate that backbone training is crucial for good generalisation in FSC across different adaptation methods.
Rho-1: Not All Tokens Are What You Need
Venue: arXiv cs.CL 2025
Year: 2025

Previous language model pre-training methods have uniformly applied a next-token prediction loss to all training tokens. Challenging this norm, we posit that "9l training". Our initial analysis examines token-level training dynamics of language model, revealing distinct loss patterns for different tokens. Leveraging these insights, we introduce a new language model called Rho-1. Unlike traditional LMs that learn to predict every next token in a corpus, Rho-1 employs Selective Language Modeling (SLM), which selectively trains on useful tokens that aligned with the desired distribution. This approach involves scoring pretraining tokens using a reference model, and then training the language model with a focused loss on tokens with higher scores. When continual pretraining on 15B OpenWebMath corpus, Rho-1 yields an absolute improvement in few-shot accuracy of up to 30% in 9 math tasks. After fine-tuning, Rho-1-1B and 7B achieved state-of-the-art results of 40.6% and 51.8% on MATH dataset, respectively - matching DeepSeekMath with only 3% of the pretraining tokens. Furthermore, when continual pretraining on 80B general tokens, Rho-1 achieves 6.8% average enhancement across 15 diverse tasks, increasing both efficiency and performance of the language model pre-training.
WildGraph: Realistic Graph-based Trajectory Generation for Wildlife
Venue: arXiv cs.LG 2025
Year: 2025

Trajectory generation is an important task in movement studies; it circumvents the privacy, ethical, and technical challenges of collecting real trajectories from the target population. In particular, real trajectories in the wildlife domain are scarce as a result of ethical and environmental constraints of the collection process. In this paper, we consider the problem of generating long-horizon trajectories, akin to wildlife migration, based on a small set of real samples. We propose a hierarchical approach to learn the global movement characteristics of the real dataset and recursively refine localized regions. Our solution, WildGraph, discretizes the geographic path into a prototype network of H3 (https://www.uber.com/blog/h3/) regions and leverages a recurrent variational auto-encoder to probabilistically generate paths over the regions, based on occupancy. WildGraph successfully generates realistic months-long trajectories using a sample size as small as 60. Experiments performed on two wildlife migration datasets demonstrate that our proposed method improves the generalization of the generated trajectories in comparison to existing work while achieving superior or comparable performance in several benchmark metrics. Our code is published on the following repository: https://github.com/aliwister/wildgraph.
Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis. However, current tabular diffusion models tend to inherit bias in the training dataset and generate biased synthetic data, which may influence discriminatory actions. In this research, we introduce a novel tabular diffusion model that incorporates sensitive guidance to generate fair synthetic data with balanced joint distributions of the target label and sensitive attributes, such as sex and race. The empirical results demonstrate that our method effectively mitigates bias in training data while maintaining the quality of the generated samples. Furthermore, we provide evidence that our approach outperforms existing methods for synthesizing tabular data on fairness metrics such as demographic parity ratio and equalized odds ratio, achieving improvements of over $10\%$. Our implementation is available at https://github.com/comp-well-org/fair-tab-diffusion.
Collective Bayesian Decision-Making in a Swarm of Miniaturized Robots for Surface Inspection
Venue: arXiv cs.RO 2025
Year: 2025

Robot swarms can effectively serve a variety of sensing and inspection applications. Certain inspection tasks require a binary classification decision. This work presents an experimental setup for a surface inspection task based on vibration sensing and studies a Bayesian two-outcome decision-making algorithm in a swarm of miniaturized wheeled robots. The robots are tasked with individually inspecting and collectively classifying a 1mx1m tiled surface consisting of vibrating and non-vibrating tiles based on the majority type of tiles. The robots sense vibrations using onboard IMUs and perform collision avoidance using a set of IR sensors. We develop a simulation and optimization framework leveraging the Webots robotic simulator and a Particle Swarm Optimization (PSO) method. We consider two existing information sharing strategies and propose a new one that allows the swarm to rapidly reach accurate classification decisions. We first find optimal parameters that allow efficient sampling in simulation and then evaluate our proposed strategy against the two existing ones using 100 randomized simulation and 10 real experiments. We find that our proposed method compels the swarm to make decisions at an accelerated rate, with an improvement of up to 20.52% in mean decision time at only 0.78% loss in accuracy.
A Complexity Map of Probabilistic Reasoning for Neurosymbolic Classification Techniques
Venue: arXiv cs.AI 2025
Year: 2025

Neurosymbolic artificial intelligence is a growing field of research aiming to combine neural network learning capabilities with the reasoning abilities of symbolic systems. Informed multi-label classification is a sub-field of neurosymbolic AI which studies how to leverage prior knowledge to improve neural classification systems. Recently, a family of neurosymbolic techniques for informed classification based on probabilistic reasoning has gained significant traction. Unfortunately, depending on the language used to represent prior knowledge, solving certain probabilistic reasoning problems can become prohibitively hard when the number of classes increases. Therefore, the asymptotic complexity of probabilistic reasoning is of cardinal importance to assess the scalability of such techniques. In this paper, we develop a unified formalism for four probabilistic reasoning problems. Then, we compile several known and new tractability results into a single complexity map of probabilistic reasoning. We build on top of this complexity map to characterize the domains of scalability of several techniques. We hope this work will help neurosymbolic AI practitioners navigate the scalability landscape of probabilistic neurosymbolic techniques.
Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task
Venue: arXiv cs.RO 2025
Year: 2025

Human intention-based systems enable robots to perceive and interpret user actions to interact with humans and adapt to their behavior proactively. Therefore, intention prediction is pivotal in creating a natural interaction with social robots in human-designed environments. In this paper, we examine using Large Language Models (LLMs) to infer human intention in a collaborative object categorization task with a physical robot. We propose a novel multimodal approach that integrates user non-verbal cues, like hand gestures, body poses, and facial expressions, with environment states and user verbal cues to predict user intentions in a hierarchical architecture. Our evaluation of five LLMs shows the potential for reasoning about verbal and non-verbal user cues, leveraging their context-understanding and real-world knowledge to support intention prediction while collaborating on a task with a social robot. Video: https://youtu.be/tBJHfAuzohI
A Survey on Patent Analysis: From NLP to Multimodal AI
Venue: arXiv cs.IR 2025
Year: 2025

Recent advances in Pretrained Language Models (PLMs) and Large Language Models (LLMs) have demonstrated transformative capabilities across diverse domains. The field of patent analysis and innovation is not an exception, where natural language processing (NLP) techniques presents opportunities to streamline and enhance important tasks -- such as patent classification and patent retrieval -- in the patent cycle. This not only accelerates the efficiency of patent researchers and applicants, but also opens new avenues for technological innovation and discovery. Our survey provides a comprehensive summary of recent NLP-based methods -- including multimodal ones -- in patent analysis. We also introduce a novel taxonomy for categorization based on tasks in the patent life cycle, as well as the specifics of the methods. This interdisciplinary survey aims to serve as a comprehensive resource for researchers and practitioners who work at the intersection of NLP, Multimodal AI, and patent analysis, as well as patent offices to build efficient patent systems.